





"""simple docstring"""





import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						lowercase   (   _UpperCAmelCase	,    unittest.TestCase   ):
      _SCREAMING_SNAKE_CASE							      = OpenAIGPTTokenizer
      _SCREAMING_SNAKE_CASE							      = OpenAIGPTTokenizerFast
      _SCREAMING_SNAKE_CASE							      = True
      _SCREAMING_SNAKE_CASE							      = False





      def        _snake_case     (			self					)						->	Dict:
            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            lowerCAmelCase    		=			[
                """l""",
                """o""",
                """w""",
                """e""",
                """r""",
                """s""",
                """t""",
                """i""",
                """d""",
                """n""",
                """w</w>""",
                """r</w>""",
                """t</w>""",
                """lo""",
                """low""",
                """er</w>""",
                """low</w>""",
                """lowest</w>""",
                """newer</w>""",
                """wider</w>""",
                """<unk>""",
            ]
            lowerCAmelCase    		=			dict(zip(lowercase ,		range(len(lowercase					)					)					)					)
            lowerCAmelCase    		=			["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]

            lowerCAmelCase    		=			os.path.join(self.tmpdirname ,		VOCAB_FILES_NAMES["""vocab_file"""]					)
            lowerCAmelCase    		=			os.path.join(self.tmpdirname ,		VOCAB_FILES_NAMES["""merges_file"""]					)
            with open(self.vocab_file ,		"""w"""					) as fp:
                  fp.write(json.dumps(lowercase					)					)
            with open(self.merges_file ,		"""w"""					) as fp:
                  fp.write("""\n""".join(lowercase					)					)





      def        _snake_case     (			self ,		lowercase					)						->	List[Any]:
            return "lower newer", "lower newer"





      def        _snake_case     (			self					)						->	List[str]:
            lowerCAmelCase    		=			OpenAIGPTTokenizer(self.vocab_file ,		self.merges_file					)

            lowerCAmelCase    		=			"""lower"""
            lowerCAmelCase    		=			["""low""", """er</w>"""]
            lowerCAmelCase    		=			tokenizer.tokenize(lowercase					)
            self.assertListEqual(lowercase ,		lowercase					)

            lowerCAmelCase    		=			tokens + ["""<unk>"""]
            lowerCAmelCase    		=			[14, 15, 20]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase					) ,		lowercase					)





      def        _snake_case     (			self ,		lowercase=15					)						->	Any:
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'					):
                        lowerCAmelCase    		=			self.rust_tokenizer_class.from_pretrained(lowercase ,		**lowercase					)

                        # Simple input
                        lowerCAmelCase    		=			"""This is a simple input"""
                        lowerCAmelCase    		=			["""This is a simple input 1""", """This is a simple input 2"""]
                        lowerCAmelCase    		=			("""This is a simple input""", """This is a pair""")
                        lowerCAmelCase    		=			[
                            ("""This is a simple input 1""", """This is a simple input 2"""),
                            ("""This is a simple pair 1""", """This is a simple pair 2"""),
                        ]

                        # Simple input tests
                        self.assertRaises(lowercase ,		tokenizer_r.encode ,		lowercase ,		max_length=lowercase ,		padding="""max_length"""					)

                        # Simple input
                        self.assertRaises(lowercase ,		tokenizer_r.encode_plus ,		lowercase ,		max_length=lowercase ,		padding="""max_length"""					)

                        # Simple input
                        self.assertRaises(
                            lowercase ,		tokenizer_r.batch_encode_plus ,		lowercase ,		max_length=lowercase ,		padding="""max_length""" ,		)

                        # Pair input
                        self.assertRaises(lowercase ,		tokenizer_r.encode ,		lowercase ,		max_length=lowercase ,		padding="""max_length"""					)

                        # Pair input
                        self.assertRaises(lowercase ,		tokenizer_r.encode_plus ,		lowercase ,		max_length=lowercase ,		padding="""max_length"""					)

                        # Pair input
                        self.assertRaises(
                            lowercase ,		tokenizer_r.batch_encode_plus ,		lowercase ,		max_length=lowercase ,		padding="""max_length""" ,		)





      def        _snake_case     (			self					)						->	Union[str, Any]:
            pass







@require_ftfy
@require_spacy
@require_tokenizers
class 						lowercase   (   _UpperCAmelCase   ):
      pass













"""simple docstring"""





import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


SCREAMING_SNAKE_CASE__   =  get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
       SCREAMING_SNAKE_CASE__   =  json.load(f)

@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self ,		lowercase					)						->	Tuple:
            return FSMTTokenizer.from_pretrained(lowercase					)





      def        _snake_case     (			self ,		lowercase					)						->	Dict:
            lowerCAmelCase    		=			FSMTForConditionalGeneration.from_pretrained(lowercase					).to(lowercase					)
            if torch_device == "cuda":
                  model.half()
            return model





      @parameterized.expand(
          [
              ["""en-ru""", 26.0],
              ["""ru-en""", 22.0],
              ["""en-de""", 22.0],
              ["""de-en""", 29.0],
          ]					)
      @slow
      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	Dict:
            # note: this test is not testing the best performance since it only evals a small batch
            # but it should be enough to detect a regression in the output quality
            lowerCAmelCase    		=			f'facebook/wmt19-{pair}'
            lowerCAmelCase    		=			self.get_tokenizer(lowercase					)
            lowerCAmelCase    		=			self.get_model(lowercase					)

            lowerCAmelCase    		=			bleu_data[pair]["""src"""]
            lowerCAmelCase    		=			bleu_data[pair]["""tgt"""]

            lowerCAmelCase    		=			tokenizer(lowercase ,		return_tensors="""pt""" ,		truncation=lowercase ,		padding="""longest"""					).to(lowercase					)
            lowerCAmelCase    		=			model.generate(
                input_ids=batch.input_ids ,		num_beams=8 ,		)
            lowerCAmelCase    		=			tokenizer.batch_decode(
                lowercase ,		skip_special_tokens=lowercase ,		clean_up_tokenization_spaces=lowercase					)
            lowerCAmelCase    		=			calculate_bleu(lowercase ,		lowercase					)
            print(lowercase					)
            self.assertGreaterEqual(scores["""bleu"""] ,		lowercase					)






